Abstract

A lightweight convolutional (deep) neural networks (CNNs) based modulation format identification (MFI) scheme in 2D Stokes planes for polarization domain multiplexing (PDM) fiber communication system is proposed and demonstrated. Influences of the learning rate of CNN is discussed. Experimental verifications are performed for the PDM system at a symbol rate of 28GBaud. Six modulation formats are identified with a trained CNN from images of received signals. They are PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. By taking advantage of computer vision, the results show that the proposed scheme can significantly improve the identification performance over the existing techniques.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Multi-task deep neural network (MT-DNN) enabled optical performance monitoring from directly detected PDM-QAM signals

Yijun Cheng, Songnian Fu, Ming Tang, and Deming Liu
Opt. Express 27(13) 19062-19074 (2019)

Modulation format identification and OSNR monitoring using density distributions in Stokes axes for digital coherent receivers

Anlin Yi, Lianshan Yan, Hengjiang Liu, Lin Jiang, Yan Pan, Bin Luo, and Wei Pan
Opt. Express 27(4) 4471-4479 (2019)

Modulation format identification assisted by sparse-fast-Fourier-transform for hitless flexible coherent transceivers

Jianing Lu, Zhongwei Tan, Alan Pak Tao Lau, Songnian Fu, Ming Tang, and Chao Lu
Opt. Express 27(5) 7072-7086 (2019)

References

  • View by:
  • |
  • |
  • |

  1. Q. Zhuge, M. Morsy-Osman, X. Xu, M. Chagnon, M. Qiu, and D. V. Plant, “Spectral Efficiency-Adaptive Optical TransmissionUsing Time Domain Hybrid QAM for Agile Optical Networks,” J. Lightwave Technol. 31(15), 2621–2628 (2013).
    [Crossref]
  2. A. Nag, M. Tornatore, and B. Mukherjee, “Optical Network Design With Mixed Line Rates and Multiple Modulation Formats,” J. Lightwave Technol. 28(4), 466–475 (2010).
    [Crossref]
  3. Z. Zhang and C. Li, “Hitless Multi-rate Coherent Transceiver,” in Advanced Photonics 2015, OSA Technical Digest (online) (Optical Society of America, 2015), SpS3D.2.
  4. A. K. Nandi and E. E. Azzouz, “Automatic analogue modulation recognition,” Signal Process. 46(2), 211–222 (1995).
    [Crossref]
  5. M. Xiang, Q. Zhuge, M. Qiu, X. Zhou, F. Zhang, M. Tang, D. Liu, S. Fu, and D. V. Plant, “Modulation format identification aided hitless flexible coherent transceiver,” Opt. Express 24(14), 15642–15655 (2016).
    [Crossref] [PubMed]
  6. R. Boada, R. Borkowski, and I. T. Monroy, “Clustering algorithms for Stokes space modulation format recognition,” Opt. Express 23(12), 15521–15531 (2015).
    [Crossref] [PubMed]
  7. L. Cheng, L. Xi, D. Zhao, X. Tang, W. Zhang, and X. Zhang, “Improved modulation format identification based on Stokes parameters using combination of fuzzy c-means and hierarchical clustering in coherent optical communication system,” Chin. Opt. Lett. 13(10), 100604 (2015).
    [Crossref]
  8. P. Isautier, A. Stark, K. Mehta, R. de Salvo, and S. E. Ralph, “Autonomous Software-Defined Coherent Optical Receivers,” in Optical Fiber Communication Conference/National Fiber Optic Engineers Conference 2013, OSA Technical Digest (online) (Optical Society of America, 2013), paper OTh3B.4.
    [Crossref]
  9. P. Isautier, J. Pan, R. DeSalvo, and S. E. Ralph, “Stokes Space-Based Modulation Format Recognition for Autonomous Optical Receivers,” J. Lightwave Technol. 33(24), 5157–5163 (2015).
    [Crossref]
  10. J. Liu, Z. Dong, K. P. Zhong, A. P. T. Lau, C. Lu, and Y. Lu, “Modulation Format Identification Based on Received Signal Power Distributions for Digital Coherent Receivers,” in Optical Fiber Communication Conference, OSA Technical Digest (online) (Optical Society of America, 2014), paper Th4D.3.
    [Crossref]
  11. L. Jiang, L. Yan, A. Yi, Y. Pan, T. Bo, M. Hao, W. Pan, and B. Luo, “Blind density-peak-based modulation format identification for elastic optical networks,” J. Lightwave Technol. 36, 2850–2858 (2018).
  12. S. M. Bilal, G. Bosco, Z. Dong, A. P. T. Lau, and C. Lu, “Blind modulation format identification for digital coherent receivers,” Opt. Express 23(20), 26769–26778 (2015).
    [Crossref] [PubMed]
  13. M. Xiang, Q. Zhuge, M. Qiu, X. Zhou, M. Tang, D. Liu, S. Fu, and D. V. Plant, “RF-pilot aided modulation format identification for hitless coherent transceiver,” Opt. Express 25(1), 463–471 (2017).
    [Crossref] [PubMed]
  14. D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
    [Crossref] [PubMed]
  15. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation,” arXiv preprint arXiv:1801.04381 (2018).
  16. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861 (2017).
  17. L. Sifre and P. Mallat, “Rigid-motion scattering for image classification,” (Citeseer, 2014).
  18. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016), 770–778.
    [Crossref]
  19. X. Mai, J. Liu, X. Wu, Q. Zhang, C. Guo, Y. Yang, and Z. Li, “Stokes space modulation format classification based on non-iterative clustering algorithm for coherent optical receivers,” Opt. Express 25(3), 2038–2050 (2017).
    [Crossref] [PubMed]
  20. N. J. Muga and A. N. Pinto, “Adaptive 3-D Stokes Space-Based Polarization Demultiplexing Algorithm,” J. Lightwave Technol. 32(19), 3290–3298 (2014).
    [Crossref]
  21. S. Savory, “Compensation of fibre impairments in digital coherent systems,” in 2008 34th European Conference on Optical Communication, 2008), 1–1.
  22. B. Chomycz, Planning fiber optics networks (McGraw-Hill Education Group, 2009), Chap. 7.
  23. H. G. Choi, J. H. Chang, H. Kim, and Y. C. Chung, “Nonlinearity-Tolerant OSNR Estimation Technique for Coherent Optical Systems,” in Optical Fiber Communication Conference, OSA Technical Digest (online) (Optical Society of America, 2015), W4D.2.
    [Crossref]

2018 (1)

2017 (3)

2016 (1)

2015 (4)

2014 (1)

2013 (1)

2010 (1)

1995 (1)

A. K. Nandi and E. E. Azzouz, “Automatic analogue modulation recognition,” Signal Process. 46(2), 211–222 (1995).
[Crossref]

Azzouz, E. E.

A. K. Nandi and E. E. Azzouz, “Automatic analogue modulation recognition,” Signal Process. 46(2), 211–222 (1995).
[Crossref]

Bilal, S. M.

Bo, T.

Boada, R.

Borkowski, R.

Bosco, G.

Chagnon, M.

Chen, X.

Cheng, L.

DeSalvo, R.

Dong, Z.

Fu, S.

Guo, C.

Hao, M.

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016), 770–778.
[Crossref]

Isautier, P.

Jiang, L.

Lau, A. P. T.

Li, J.

Li, Z.

Liu, D.

Liu, J.

Lu, C.

Luo, B.

Mai, X.

Monroy, I. T.

Morsy-Osman, M.

Muga, N. J.

Mukherjee, B.

Nag, A.

Nandi, A. K.

A. K. Nandi and E. E. Azzouz, “Automatic analogue modulation recognition,” Signal Process. 46(2), 211–222 (1995).
[Crossref]

Pan, J.

Pan, W.

Pan, Y.

Pinto, A. N.

Plant, D. V.

Qiu, M.

Ralph, S. E.

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016), 770–778.
[Crossref]

Song, C.

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016), 770–778.
[Crossref]

Tang, M.

Tang, X.

Tornatore, M.

Wang, D.

Wu, X.

Xi, L.

Xiang, M.

Xu, X.

Yan, L.

Yang, Y.

Yi, A.

Zhang, F.

Zhang, M.

Zhang, Q.

Zhang, W.

Zhang, X.

Zhao, D.

Zhou, X.

Zhuge, Q.

Chin. Opt. Lett. (1)

J. Lightwave Technol. (5)

Opt. Express (6)

Signal Process. (1)

A. K. Nandi and E. E. Azzouz, “Automatic analogue modulation recognition,” Signal Process. 46(2), 211–222 (1995).
[Crossref]

Other (10)

S. Savory, “Compensation of fibre impairments in digital coherent systems,” in 2008 34th European Conference on Optical Communication, 2008), 1–1.

B. Chomycz, Planning fiber optics networks (McGraw-Hill Education Group, 2009), Chap. 7.

H. G. Choi, J. H. Chang, H. Kim, and Y. C. Chung, “Nonlinearity-Tolerant OSNR Estimation Technique for Coherent Optical Systems,” in Optical Fiber Communication Conference, OSA Technical Digest (online) (Optical Society of America, 2015), W4D.2.
[Crossref]

Z. Zhang and C. Li, “Hitless Multi-rate Coherent Transceiver,” in Advanced Photonics 2015, OSA Technical Digest (online) (Optical Society of America, 2015), SpS3D.2.

J. Liu, Z. Dong, K. P. Zhong, A. P. T. Lau, C. Lu, and Y. Lu, “Modulation Format Identification Based on Received Signal Power Distributions for Digital Coherent Receivers,” in Optical Fiber Communication Conference, OSA Technical Digest (online) (Optical Society of America, 2014), paper Th4D.3.
[Crossref]

P. Isautier, A. Stark, K. Mehta, R. de Salvo, and S. E. Ralph, “Autonomous Software-Defined Coherent Optical Receivers,” in Optical Fiber Communication Conference/National Fiber Optic Engineers Conference 2013, OSA Technical Digest (online) (Optical Society of America, 2013), paper OTh3B.4.
[Crossref]

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation,” arXiv preprint arXiv:1801.04381 (2018).

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861 (2017).

L. Sifre and P. Mallat, “Rigid-motion scattering for image classification,” (Citeseer, 2014).

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016), 770–778.
[Crossref]

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (11)

Fig. 1
Fig. 1 Images of constellations generated by using 20000 received signals in PDM optical fiber communication system at 25dB OSNR. From the left to the right, the value of azimuth angle becomes larger and larger. The laser sources are working at a frequency of 193.3THz with linewidth of 100 kHz.
Fig. 2
Fig. 2 Images of constellations generated by using received 20000 signals for different modulation formats. Without prior information about the modulation format of the received signals, it is very challenging to distinguish them from each other, even when the OSNR is relatively high (25dB).
Fig. 3
Fig. 3 Images of 20000 symbols of PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM signals in 2D Stokes planes with their corresponding 3D Stokes space constellations. Images in the first column are Jones constellations of the corresponding signals.
Fig. 4
Fig. 4 The simulation platform setup.
Fig. 5
Fig. 5 Identifying the modulation format for a sequence of modulated signals by converting the sequence to an image combination of 2-D images and classifying the image combination with MobileNet V2.
Fig. 6
Fig. 6 Identification accuracy at different OSNRs (9dB to 35dB) with image size 224 × 224 pixels.
Fig. 7
Fig. 7 Images of PDM-16QAM and PDM-64QAM in three different 2D Stokes planes at OSNR 15dB.
Fig. 8
Fig. 8 Identification accuracy for input images with different resolutions of 56 × 56 pixels, 112 × 112 pixels, 224 × 224 pixels, each resolution contains six modulation formats.
Fig. 9
Fig. 9 Identification accuracy for input images of different number of symbols from 5000 to 20000 at the step of 5000 with different resolutions.
Fig. 10
Fig. 10 The total accuracy at different epochs for input images with different resolutions of 56 × 56 pixels, 112 × 112 pixels, 224 × 224 pixels.
Fig. 11
Fig. 11 Identification accuracy under residual CD values from −300 to 300 ps/nm for PDM-16QAM (19dB) signal and PDM-64QAM (25dB) signal.

Tables (2)

Tables Icon

Table 1 Parameters of the MobileNet V2 network

Tables Icon

Table 2 Number of test image combinations and accuracy matrix for the proposed CNN-MFI scheme with image size 224 × 224. Each modulation format contains 2100 test image combinations (OSNR from 15dB to 35dB). Five image combinations of PDM-16QAM are misclassified as PDM-64QAM (at OSNR 15dB). The total accuracy is 99.96%.

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

Γ=[ cosθ e jΔ sinθ e jΔ sinθ cosθ ], θ[ 0°, 90° ],Δ[ 0°,360° ]
S=( s 0 s 1 s 2 s 3 )=( e x e x * + e y e y * e x e x * e y e y * e y e x * + e x e y * j e y e x * +j e x e y * )=( a x 2 + a y 2 a x 2 a y 2 2 a x a y cosδ 2 a x a y sinδ )
OSNR= P ch P ASE + P NID ,

Metrics